RNAseq
Quality check from Kallisto output
This information is to be coupled with the multiQC report generated by the pipeline. The percentage of pseudoaligned reads is more homogeneous and higher when the paired-end protocol is used. Looking at all the figures generated, the results seem to be of better quality when using the paired-end protocol.
Protocol
n_targets
n_processed
n_pseudoaligned
n_unique
p_pseudoaligned
p_unique
Protocol + Condition
n_targets
n_processed
n_pseudoaligned
n_unique
p_pseudoaligned
p_unique
Differental Expression : Treated vs Untreated
Single-end
Results :
- DEGs number : 1931 (padj < 0.05)
- Up-regulated in Treated condition : 1055
- Down-regulated in Treated condition : 876
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Dataset
Liste
Outlier
Heatmap
PCA
Expression
Volcano
Top20
TopHeatmap
MDplot
Paired-end
Results :
- DEGs number : 3346 (padj < 0.05)
- Up-regulated in Treated condition : 1773
- Down-regulated in Treated condition : 1573
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Dataset
Liste
Outlier
Heatmap
PCA
Expression
Volcano
Top20
TopHeatmap
MDplot
Comparaison Single-end vs Paired-end
Intersection of the DEA result
There are more differentially expressed transcripts detected between the treated and untreated conditions using the paired-end protocol. The paired-end protocol allows us to obtain both homogeneous samples and more biological information, as shown in the Kallisto output.
Furthermore, when a PCA is performed with the paired-end protocol, 97% of the dataset’s variability is explained by the treated/untreated feature, compared to 89% for the single-end protocol. This indicates that the biological variability of primary interest (the treatment effect) is much more highlighted and manageable with the paired-end protocol.
Due to the clarity and uniformity of the variability explained by the treatment condition in the paired-end protocol, the DEA results become more meaningful and easier to exploit. This may also explain why a greater number of differentially expressed transcripts are identified.
The paired-end protocol provides more biological information, which in turn offers greater statistical power, higher confidence in the results, and a clearer signal of the treatment effect compared to the single-end protocol.
Conversely, the single-end protocol introduces more noise into the results, making it less suitable for this type of analysis.
In conclusion, there is a clear treatment effect on the sequenced individuals, regardless of the protocol used. However, this effect is better highlighted when the paired-end protocol is employed.
Multivariate model (EXPERIMENTAL)
Dataset
Outlier
Heatmap
PCA
MDplot
Treated vs Untreated
Results :
- DEGs number : 4106 (padj < 0.05)
- Up-regulated in Treated condition : 2034
- Down-regulated in Treated condition : 2072
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Expression
Volcano
Top20
TopHeatmap
Single-end vs Paired-end
Results :
- DEGs number : 5931 (padj < 0.05)
- Up-regulated in Treated condition : 2457
- Down-regulated in Treated condition : 3474
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition